In the race for AI studios, IBM has also put its ticket into the lot. IBM has introduced its next-generation artificial intelligence studio Watson. It is an enterprise-ready AI and data platform.
Let us delve into the components that make it work:
Watson.data : Ibm’s curated repository of massive data that can be accessed to train models.
http://Watsonx.ai : This has further subcomponents-
train- feed data sets into your model and enhance its understanding.
validate- to ensure that your model is giving the right outcomes for unseen inputs. It helps to quantify the performance.
tune- Tuning allows you to run trial errors to capture any erroneous results. You can alter your parameters according to it.
deploy – By putting your model into production, you can build an enterprise-grade deployment.
Watsonx.governance – makes sure that the AI is responsible.
Features
Foundation models – open source models of IBM are available for clients. You can build any complex model with minimal effort.
Prompt lab -Allows for NLP(Natural language processing) tasks. prompting types available: one-shot, zero-shot, few-shot.
Tuning studio
Data Science and MLOps
API connectivity
Data Joining, Cleansing, Sharing and modelling
Data profiling and classification.
Future expectations
Plans of integration with all IBM software for interoperability.
Coding assistant that will allow complex coding with simple commands.
Environmental intelligence suite: will let organizations mitigate environmental risks according to their needs.
AI Operations (AIOps) Insights.
IBM has developed this to primarily cater to businesses that want to leverage AI to scale their operations. It aims at reducing redundancy, improving speed and output, helps with quick customer service, lets HR complete repetitive tasks, etc. They want to transform the way your business functions.
